Overview

Brought to you by YData

Dataset statistics

Number of variables27
Number of observations22251
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.2 MiB
Average record size in memory530.0 B

Variable types

Categorical6
Numeric19
Boolean2

Alerts

Date_hour has constant value "0.0" Constant
Date_minute has constant value "0.0" Constant
Cloud3pm is highly overall correlated with Cloud9am and 2 other fieldsHigh correlation
Cloud9am is highly overall correlated with Cloud3pm and 2 other fieldsHigh correlation
Evaporation is highly overall correlated with Humidity9am and 4 other fieldsHigh correlation
Humidity3pm is highly overall correlated with Cloud3pm and 4 other fieldsHigh correlation
Humidity9am is highly overall correlated with Evaporation and 2 other fieldsHigh correlation
MaxTemp is highly overall correlated with Evaporation and 3 other fieldsHigh correlation
MinTemp is highly overall correlated with Evaporation and 5 other fieldsHigh correlation
Pressure3pm is highly overall correlated with MinTemp and 2 other fieldsHigh correlation
Pressure9am is highly overall correlated with MinTemp and 1 other fieldsHigh correlation
RainTomorrow is highly overall correlated with Humidity3pmHigh correlation
Sunshine is highly overall correlated with Cloud3pm and 4 other fieldsHigh correlation
Temp3pm is highly overall correlated with Evaporation and 4 other fieldsHigh correlation
Temp9am is highly overall correlated with Evaporation and 4 other fieldsHigh correlation
WindGustSpeed is highly overall correlated with WindSpeed3pm and 1 other fieldsHigh correlation
WindSpeed3pm is highly overall correlated with WindGustSpeedHigh correlation
WindSpeed9am is highly overall correlated with WindGustSpeedHigh correlation
Rainfall has 14448 (64.9%) zeros Zeros
Sunshine has 657 (3.0%) zeros Zeros
Cloud9am has 2072 (9.3%) zeros Zeros
Cloud3pm has 1409 (6.3%) zeros Zeros
Date_weekday has 3154 (14.2%) zeros Zeros

Reproduction

Analysis started2025-11-03 20:53:06.501438
Analysis finished2025-11-03 20:54:22.830866
Duration1 minute and 16.33 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Location
Categorical

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Darwin
 
1202
Perth
 
1195
Brisbane
 
1171
MelbourneAirport
 
1148
PerthAirport
 
1145
Other values (21)
16390 

Length

Max length16
Median length11
Mean length9.0179767
Min length4

Characters and Unicode

Total characters200659
Distinct characters35
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCobar
2nd rowCobar
3rd rowCobar
4th rowCobar
5th rowCobar

Common Values

ValueCountFrequency (%)
Darwin 1202
 
5.4%
Perth 1195
 
5.4%
Brisbane 1171
 
5.3%
MelbourneAirport 1148
 
5.2%
PerthAirport 1145
 
5.1%
SydneyAirport 1143
 
5.1%
Watsonia 1055
 
4.7%
Mildura 1015
 
4.6%
MountGambier 968
 
4.4%
WaggaWagga 967
 
4.3%
Other values (16) 11242
50.5%

Length

2025-11-04T02:24:22.944150image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
darwin 1202
 
5.4%
perth 1195
 
5.4%
brisbane 1171
 
5.3%
melbourneairport 1148
 
5.2%
perthairport 1145
 
5.1%
sydneyairport 1143
 
5.1%
watsonia 1055
 
4.7%
mildura 1015
 
4.6%
mountgambier 968
 
4.4%
waggawagga 967
 
4.3%
Other values (16) 11242
50.5%

Most occurring characters

ValueCountFrequency (%)
r 24186
 
12.1%
a 17995
 
9.0%
o 17240
 
8.6%
e 15203
 
7.6%
i 14308
 
7.1%
n 13490
 
6.7%
t 10579
 
5.3%
l 9937
 
5.0%
s 6534
 
3.3%
b 5971
 
3.0%
Other values (25) 65216
32.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 170643
85.0%
Uppercase Letter 30016
 
15.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 24186
14.2%
a 17995
10.5%
o 17240
10.1%
e 15203
8.9%
i 14308
8.4%
n 13490
 
7.9%
t 10579
 
6.2%
l 9937
 
5.8%
s 6534
 
3.8%
b 5971
 
3.5%
Other values (12) 35200
20.6%
Uppercase Letter
ValueCountFrequency (%)
M 4633
15.4%
A 4327
14.4%
W 4156
13.8%
S 3354
11.2%
P 3069
10.2%
C 2153
7.2%
N 1765
 
5.9%
H 1310
 
4.4%
D 1202
 
4.0%
B 1171
 
3.9%
Other values (3) 2876
9.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 200659
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 24186
 
12.1%
a 17995
 
9.0%
o 17240
 
8.6%
e 15203
 
7.6%
i 14308
 
7.1%
n 13490
 
6.7%
t 10579
 
5.3%
l 9937
 
5.0%
s 6534
 
3.3%
b 5971
 
3.0%
Other values (25) 65216
32.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 200659
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 24186
 
12.1%
a 17995
 
9.0%
o 17240
 
8.6%
e 15203
 
7.6%
i 14308
 
7.1%
n 13490
 
6.7%
t 10579
 
5.3%
l 9937
 
5.0%
s 6534
 
3.3%
b 5971
 
3.0%
Other values (25) 65216
32.5%

MinTemp
Real number (ℝ)

High correlation 

Distinct344
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.470037
Minimum-5.8
Maximum31.4
Zeros10
Zeros (%)< 0.1%
Negative219
Negative (%)1.0%
Memory size174.0 KiB
2025-11-04T02:24:23.092208image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-5.8
5-th percentile3.3
Q18.5
median13.2
Q318.5
95-th percentile24.3
Maximum31.4
Range37.2
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.4685592
Coefficient of variation (CV)0.48021836
Kurtosis-0.72062339
Mean13.470037
Median Absolute Deviation (MAD)5
Skewness0.043760837
Sum299721.8
Variance41.842258
MonotonicityNot monotonic
2025-11-04T02:24:23.263416image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.7 144
 
0.6%
8.9 141
 
0.6%
13 139
 
0.6%
10.7 139
 
0.6%
11 138
 
0.6%
13.4 135
 
0.6%
7.2 134
 
0.6%
12.5 134
 
0.6%
9.1 134
 
0.6%
14.7 133
 
0.6%
Other values (334) 20880
93.8%
ValueCountFrequency (%)
-5.8 1
< 0.1%
-5.2 2
< 0.1%
-4.9 1
< 0.1%
-4.7 2
< 0.1%
-4.4 1
< 0.1%
-4.3 1
< 0.1%
-4 1
< 0.1%
-3.9 1
< 0.1%
-3.8 1
< 0.1%
-3.7 1
< 0.1%
ValueCountFrequency (%)
31.4 1
 
< 0.1%
29.8 1
 
< 0.1%
29.7 1
 
< 0.1%
29.6 1
 
< 0.1%
29.4 1
 
< 0.1%
29.2 2
< 0.1%
29.1 1
 
< 0.1%
29 1
 
< 0.1%
28.9 1
 
< 0.1%
28.8 4
< 0.1%

MaxTemp
Real number (ℝ)

High correlation 

Distinct382
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.2084
Minimum4.1
Maximum47.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size174.0 KiB
2025-11-04T02:24:23.429092image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum4.1
5-th percentile13.4
Q118.7
median23.9
Q329.7
95-th percentile35.6
Maximum47.3
Range43.2
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.0425451
Coefficient of variation (CV)0.29091329
Kurtosis-0.67301051
Mean24.2084
Median Absolute Deviation (MAD)5.5
Skewness0.17527669
Sum538661.1
Variance49.597442
MonotonicityNot monotonic
2025-11-04T02:24:23.582997image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.4 135
 
0.6%
25.6 121
 
0.5%
22.2 121
 
0.5%
24.7 119
 
0.5%
20.1 117
 
0.5%
19 117
 
0.5%
22.9 117
 
0.5%
20.2 116
 
0.5%
21.7 116
 
0.5%
23.6 116
 
0.5%
Other values (372) 21056
94.6%
ValueCountFrequency (%)
4.1 1
< 0.1%
6.3 1
< 0.1%
7 1
< 0.1%
7.1 1
< 0.1%
7.2 1
< 0.1%
7.3 1
< 0.1%
7.6 2
< 0.1%
7.7 1
< 0.1%
7.8 1
< 0.1%
7.9 1
< 0.1%
ValueCountFrequency (%)
47.3 1
< 0.1%
46.8 2
< 0.1%
46.7 2
< 0.1%
46.4 1
< 0.1%
46.3 1
< 0.1%
46.2 1
< 0.1%
46 1
< 0.1%
45.6 1
< 0.1%
45.2 1
< 0.1%
44.9 2
< 0.1%

Rainfall
Real number (ℝ)

Zeros 

Distinct312
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.080338
Minimum0
Maximum182.6
Zeros14448
Zeros (%)64.9%
Negative0
Negative (%)0.0%
Memory size174.0 KiB
2025-11-04T02:24:23.729576image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.6
95-th percentile12
Maximum182.6
Range182.6
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation6.8204906
Coefficient of variation (CV)3.2785493
Kurtosis85.591708
Mean2.080338
Median Absolute Deviation (MAD)0
Skewness7.2704184
Sum46289.6
Variance46.519092
MonotonicityNot monotonic
2025-11-04T02:24:23.891396image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14448
64.9%
0.2 1176
 
5.3%
0.4 630
 
2.8%
0.6 447
 
2.0%
0.8 323
 
1.5%
1 265
 
1.2%
1.2 262
 
1.2%
1.4 219
 
1.0%
1.6 212
 
1.0%
2 183
 
0.8%
Other values (302) 4086
 
18.4%
ValueCountFrequency (%)
0 14448
64.9%
0.1 7
 
< 0.1%
0.2 1176
 
5.3%
0.3 6
 
< 0.1%
0.4 630
 
2.8%
0.6 447
 
2.0%
0.7 2
 
< 0.1%
0.8 323
 
1.5%
1 265
 
1.2%
1.2 262
 
1.2%
ValueCountFrequency (%)
182.6 1
< 0.1%
129.4 1
< 0.1%
128.2 1
< 0.1%
128 1
< 0.1%
121.4 1
< 0.1%
114.4 1
< 0.1%
98 1
< 0.1%
95.8 1
< 0.1%
95.2 1
< 0.1%
94.4 1
< 0.1%

Evaporation
Real number (ℝ)

High correlation 

Distinct199
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5496832
Minimum0
Maximum81.2
Zeros62
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size174.0 KiB
2025-11-04T02:24:24.076391image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12.8
median5
Q37.6
95-th percentile12
Maximum81.2
Range81.2
Interquartile range (IQR)4.8

Descriptive statistics

Standard deviation3.7045554
Coefficient of variation (CV)0.66752556
Kurtosis19.603494
Mean5.5496832
Median Absolute Deviation (MAD)2.4
Skewness2.1852155
Sum123486
Variance13.723731
MonotonicityNot monotonic
2025-11-04T02:24:24.261299image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 870
 
3.9%
8 690
 
3.1%
2.6 556
 
2.5%
2.2 541
 
2.4%
1.8 532
 
2.4%
3 530
 
2.4%
3.4 524
 
2.4%
3.8 508
 
2.3%
3.2 507
 
2.3%
2 504
 
2.3%
Other values (189) 16489
74.1%
ValueCountFrequency (%)
0 62
 
0.3%
0.2 138
 
0.6%
0.4 177
0.8%
0.6 262
1.2%
0.8 295
1.3%
0.9 3
 
< 0.1%
1 369
1.7%
1.1 5
 
< 0.1%
1.2 411
1.8%
1.3 5
 
< 0.1%
ValueCountFrequency (%)
81.2 1
< 0.1%
72.2 1
< 0.1%
54 1
< 0.1%
43.6 1
< 0.1%
42.4 1
< 0.1%
42.3 1
< 0.1%
40 1
< 0.1%
39.2 1
< 0.1%
37.6 1
< 0.1%
37.4 1
< 0.1%

Sunshine
Real number (ℝ)

High correlation  Zeros 

Distinct144
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7262415
Minimum0
Maximum14.3
Zeros657
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size174.0 KiB
2025-11-04T02:24:24.449641image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q15.1
median8.7
Q310.7
95-th percentile12.7
Maximum14.3
Range14.3
Interquartile range (IQR)5.6

Descriptive statistics

Standard deviation3.7507503
Coefficient of variation (CV)0.485456
Kurtosis-0.74926897
Mean7.7262415
Median Absolute Deviation (MAD)2.5
Skewness-0.57096687
Sum171916.6
Variance14.068128
MonotonicityNot monotonic
2025-11-04T02:24:24.623447image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 657
 
3.0%
10.8 364
 
1.6%
10.9 349
 
1.6%
10.7 345
 
1.6%
10.2 339
 
1.5%
9.8 338
 
1.5%
10.3 333
 
1.5%
10.5 330
 
1.5%
10 325
 
1.5%
10.6 320
 
1.4%
Other values (134) 18551
83.4%
ValueCountFrequency (%)
0 657
3.0%
0.1 165
 
0.7%
0.2 161
 
0.7%
0.3 111
 
0.5%
0.4 104
 
0.5%
0.5 95
 
0.4%
0.6 82
 
0.4%
0.7 127
 
0.6%
0.8 84
 
0.4%
0.9 94
 
0.4%
ValueCountFrequency (%)
14.3 2
 
< 0.1%
14.2 2
 
< 0.1%
14.1 1
 
< 0.1%
14 3
 
< 0.1%
13.9 5
 
< 0.1%
13.8 14
 
0.1%
13.7 41
0.2%
13.6 54
0.2%
13.5 43
0.2%
13.4 74
0.3%

WindGustDir
Categorical

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
E
1840 
SW
1637 
ENE
1616 
W
1610 
N
1590 
Other values (11)
13958 

Length

Max length3
Median length2
Mean length2.1685318
Min length1

Characters and Unicode

Total characters48252
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSSW
2nd rowS
3rd rowNNE
4th rowWNW
5th rowWNW

Common Values

ValueCountFrequency (%)
E 1840
 
8.3%
SW 1637
 
7.4%
ENE 1616
 
7.3%
W 1610
 
7.2%
N 1590
 
7.1%
SE 1588
 
7.1%
SSW 1535
 
6.9%
WSW 1495
 
6.7%
S 1462
 
6.6%
SSE 1367
 
6.1%
Other values (6) 6511
29.3%

Length

2025-11-04T02:24:25.044386image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e 1840
 
8.3%
sw 1637
 
7.4%
ene 1616
 
7.3%
w 1610
 
7.2%
n 1590
 
7.1%
se 1588
 
7.1%
ssw 1535
 
6.9%
wsw 1495
 
6.7%
s 1462
 
6.6%
sse 1367
 
6.1%
Other values (6) 6511
29.3%

Most occurring characters

ValueCountFrequency (%)
S 13310
27.6%
E 12923
26.8%
W 11873
24.6%
N 10146
21.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 48252
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 13310
27.6%
E 12923
26.8%
W 11873
24.6%
N 10146
21.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 48252
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 13310
27.6%
E 12923
26.8%
W 11873
24.6%
N 10146
21.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 13310
27.6%
E 12923
26.8%
W 11873
24.6%
N 10146
21.0%

WindGustSpeed
Real number (ℝ)

High correlation 

Distinct56
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.103771
Minimum11
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size174.0 KiB
2025-11-04T02:24:25.221486image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile22
Q131
median39
Q348
95-th percentile67
Maximum113
Range102
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.287208
Coefficient of variation (CV)0.32326008
Kurtosis1.3759362
Mean41.103771
Median Absolute Deviation (MAD)8
Skewness0.91868905
Sum914600
Variance176.54991
MonotonicityNot monotonic
2025-11-04T02:24:25.389716image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 1533
 
6.9%
39 1495
 
6.7%
37 1460
 
6.6%
31 1402
 
6.3%
41 1382
 
6.2%
33 1376
 
6.2%
30 1125
 
5.1%
43 1106
 
5.0%
28 965
 
4.3%
48 918
 
4.1%
Other values (46) 9489
42.6%
ValueCountFrequency (%)
11 4
 
< 0.1%
13 34
 
0.2%
15 78
 
0.4%
17 163
 
0.7%
19 193
 
0.9%
20 321
 
1.4%
22 376
 
1.7%
24 536
2.4%
26 699
3.1%
28 965
4.3%
ValueCountFrequency (%)
113 1
 
< 0.1%
111 1
 
< 0.1%
109 1
 
< 0.1%
107 2
 
< 0.1%
106 6
< 0.1%
104 3
 
< 0.1%
102 3
 
< 0.1%
100 7
< 0.1%
98 11
< 0.1%
96 10
< 0.1%

WindDir9am
Categorical

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
N
1855 
E
1802 
SE
1614 
SSE
1540 
ENE
1519 
Other values (11)
13921 

Length

Max length3
Median length2
Mean length2.1710934
Min length1

Characters and Unicode

Total characters48309
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowENE
2nd rowSSE
3rd rowNNE
4th rowWNW
5th rowNW

Common Values

ValueCountFrequency (%)
N 1855
 
8.3%
E 1802
 
8.1%
SE 1614
 
7.3%
SSE 1540
 
6.9%
ENE 1519
 
6.8%
W 1486
 
6.7%
ESE 1388
 
6.2%
SW 1382
 
6.2%
NE 1378
 
6.2%
S 1342
 
6.0%
Other values (6) 6945
31.2%

Length

2025-11-04T02:24:25.581243image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n 1855
 
8.3%
e 1802
 
8.1%
se 1614
 
7.3%
sse 1540
 
6.9%
ene 1519
 
6.8%
w 1486
 
6.7%
ese 1388
 
6.2%
sw 1382
 
6.2%
ne 1378
 
6.2%
s 1342
 
6.0%
Other values (6) 6945
31.2%

Most occurring characters

ValueCountFrequency (%)
E 13357
27.6%
S 12337
25.5%
N 11703
24.2%
W 10912
22.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 48309
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 13357
27.6%
S 12337
25.5%
N 11703
24.2%
W 10912
22.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 48309
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 13357
27.6%
S 12337
25.5%
N 11703
24.2%
W 10912
22.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48309
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 13357
27.6%
S 12337
25.5%
N 11703
24.2%
W 10912
22.6%

WindDir3pm
Categorical

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
SE
1708 
S
1654 
ENE
1599 
SW
1595 
WSW
1539 
Other values (11)
14156 

Length

Max length3
Median length2
Mean length2.2032268
Min length1

Characters and Unicode

Total characters49024
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSW
2nd rowSSE
3rd rowNNW
4th rowWSW
5th rowWNW

Common Values

ValueCountFrequency (%)
SE 1708
 
7.7%
S 1654
 
7.4%
ENE 1599
 
7.2%
SW 1595
 
7.2%
WSW 1539
 
6.9%
E 1528
 
6.9%
W 1502
 
6.8%
ESE 1494
 
6.7%
SSW 1395
 
6.3%
N 1358
 
6.1%
Other values (6) 6879
30.9%

Length

2025-11-04T02:24:25.755648image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
se 1708
 
7.7%
s 1654
 
7.4%
ene 1599
 
7.2%
sw 1595
 
7.2%
wsw 1539
 
6.9%
e 1528
 
6.9%
w 1502
 
6.8%
ese 1494
 
6.7%
ssw 1395
 
6.3%
n 1358
 
6.1%
Other values (6) 6879
30.9%

Most occurring characters

ValueCountFrequency (%)
S 13454
27.4%
E 13043
26.6%
W 12095
24.7%
N 10432
21.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 49024
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 13454
27.4%
E 13043
26.6%
W 12095
24.7%
N 10432
21.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 49024
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 13454
27.4%
E 13043
26.6%
W 12095
24.7%
N 10432
21.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49024
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 13454
27.4%
E 13043
26.6%
W 12095
24.7%
N 10432
21.3%

WindSpeed9am
Real number (ℝ)

High correlation 

Distinct35
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.790886
Minimum2
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size174.0 KiB
2025-11-04T02:24:25.920780image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q19
median15
Q320
95-th percentile31
Maximum67
Range65
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.3897315
Coefficient of variation (CV)0.53130215
Kurtosis1.396793
Mean15.790886
Median Absolute Deviation (MAD)6
Skewness0.93686811
Sum351363
Variance70.387595
MonotonicityNot monotonic
2025-11-04T02:24:26.088283image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
9 2227
10.0%
13 2227
10.0%
17 2042
 
9.2%
11 1955
 
8.8%
15 1851
 
8.3%
7 1643
 
7.4%
19 1446
 
6.5%
20 1438
 
6.5%
6 1308
 
5.9%
22 956
 
4.3%
Other values (25) 5158
23.2%
ValueCountFrequency (%)
2 447
 
2.0%
4 696
 
3.1%
6 1308
5.9%
7 1643
7.4%
9 2227
10.0%
11 1955
8.8%
13 2227
10.0%
15 1851
8.3%
17 2042
9.2%
19 1446
6.5%
ValueCountFrequency (%)
67 2
 
< 0.1%
65 2
 
< 0.1%
63 1
 
< 0.1%
61 1
 
< 0.1%
57 10
< 0.1%
56 9
< 0.1%
54 14
0.1%
52 13
0.1%
50 14
0.1%
48 10
< 0.1%

WindSpeed3pm
Real number (ℝ)

High correlation 

Distinct36
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.946384
Minimum2
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size174.0 KiB
2025-11-04T02:24:26.254616image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q113
median19
Q326
95-th percentile35
Maximum76
Range74
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.5272887
Coefficient of variation (CV)0.4275105
Kurtosis0.50867889
Mean19.946384
Median Absolute Deviation (MAD)6
Skewness0.57466101
Sum443827
Variance72.714653
MonotonicityNot monotonic
2025-11-04T02:24:26.438827image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
17 1982
 
8.9%
20 1969
 
8.8%
13 1875
 
8.4%
19 1738
 
7.8%
15 1699
 
7.6%
24 1618
 
7.3%
22 1450
 
6.5%
11 1409
 
6.3%
9 1278
 
5.7%
28 1276
 
5.7%
Other values (26) 5957
26.8%
ValueCountFrequency (%)
2 71
 
0.3%
4 171
 
0.8%
6 442
 
2.0%
7 723
 
3.2%
9 1278
5.7%
11 1409
6.3%
13 1875
8.4%
15 1699
7.6%
17 1982
8.9%
19 1738
7.8%
ValueCountFrequency (%)
76 2
 
< 0.1%
65 3
 
< 0.1%
63 1
 
< 0.1%
61 3
 
< 0.1%
59 2
 
< 0.1%
57 3
 
< 0.1%
56 5
 
< 0.1%
54 7
 
< 0.1%
52 6
 
< 0.1%
50 36
0.2%

Humidity9am
Real number (ℝ)

High correlation 

Distinct100
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.669363
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size174.0 KiB
2025-11-04T02:24:26.591778image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile32
Q155
median67
Q379
95-th percentile94
Maximum100
Range99
Interquartile range (IQR)24

Descriptive statistics

Standard deviation18.407447
Coefficient of variation (CV)0.28030494
Kurtosis0.11586214
Mean65.669363
Median Absolute Deviation (MAD)12
Skewness-0.48140059
Sum1461209
Variance338.83409
MonotonicityNot monotonic
2025-11-04T02:24:26.793014image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67 550
 
2.5%
64 531
 
2.4%
68 528
 
2.4%
69 519
 
2.3%
66 506
 
2.3%
65 502
 
2.3%
62 501
 
2.3%
71 487
 
2.2%
70 487
 
2.2%
63 481
 
2.2%
Other values (90) 17159
77.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 3
 
< 0.1%
3 2
 
< 0.1%
4 8
< 0.1%
5 8
< 0.1%
6 11
< 0.1%
7 8
< 0.1%
8 13
0.1%
9 12
0.1%
10 17
0.1%
ValueCountFrequency (%)
100 164
0.7%
99 226
1.0%
98 74
 
0.3%
97 156
0.7%
96 207
0.9%
95 219
1.0%
94 206
0.9%
93 229
1.0%
92 228
1.0%
91 251
1.1%

Humidity3pm
Real number (ℝ)

High correlation 

Distinct101
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.394679
Minimum0
Maximum100
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size174.0 KiB
2025-11-04T02:24:26.953552image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q135
median50
Q363
95-th percentile85
Maximum100
Range100
Interquartile range (IQR)28

Descriptive statistics

Standard deviation20.224397
Coefficient of variation (CV)0.40944486
Kurtosis-0.45448477
Mean49.394679
Median Absolute Deviation (MAD)14
Skewness0.024542771
Sum1099081
Variance409.02624
MonotonicityNot monotonic
2025-11-04T02:24:27.126924image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 500
 
2.2%
59 471
 
2.1%
53 470
 
2.1%
55 467
 
2.1%
58 464
 
2.1%
51 462
 
2.1%
57 461
 
2.1%
54 456
 
2.0%
56 453
 
2.0%
50 448
 
2.0%
Other values (91) 17599
79.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 6
 
< 0.1%
2 13
 
0.1%
3 17
 
0.1%
4 23
 
0.1%
5 40
0.2%
6 51
0.2%
7 64
0.3%
8 73
0.3%
9 85
0.4%
ValueCountFrequency (%)
100 17
 
0.1%
99 28
 
0.1%
98 16
 
0.1%
97 28
 
0.1%
96 49
0.2%
95 64
0.3%
94 76
0.3%
93 87
0.4%
92 81
0.4%
91 105
0.5%

Pressure9am
Real number (ℝ)

High correlation 

Distinct453
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1017.4065
Minimum980.5
Maximum1040
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size174.0 KiB
2025-11-04T02:24:27.273650image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum980.5
5-th percentile1006.4
Q11012.8
median1017.2
Q31022
95-th percentile1028.9
Maximum1040
Range59.5
Interquartile range (IQR)9.2

Descriptive statistics

Standard deviation6.8758927
Coefficient of variation (CV)0.0067582549
Kurtosis0.16791089
Mean1017.4065
Median Absolute Deviation (MAD)4.6
Skewness-0.03416652
Sum22638313
Variance47.2779
MonotonicityNot monotonic
2025-11-04T02:24:27.445487image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1015.9 152
 
0.7%
1017.6 146
 
0.7%
1015.6 144
 
0.6%
1017.7 142
 
0.6%
1014.6 142
 
0.6%
1016.4 141
 
0.6%
1017.1 139
 
0.6%
1016.5 139
 
0.6%
1019.1 139
 
0.6%
1019.9 138
 
0.6%
Other values (443) 20829
93.6%
ValueCountFrequency (%)
980.5 1
< 0.1%
982.2 1
< 0.1%
982.9 1
< 0.1%
984.6 2
< 0.1%
988.1 2
< 0.1%
988.2 1
< 0.1%
988.3 1
< 0.1%
988.5 1
< 0.1%
988.8 1
< 0.1%
990.2 1
< 0.1%
ValueCountFrequency (%)
1040 1
 
< 0.1%
1039.3 1
 
< 0.1%
1038.8 3
< 0.1%
1038.7 1
 
< 0.1%
1038.2 1
 
< 0.1%
1037.8 1
 
< 0.1%
1037.7 2
 
< 0.1%
1037.5 5
< 0.1%
1037.4 3
< 0.1%
1037.3 2
 
< 0.1%

Pressure3pm
Real number (ℝ)

High correlation 

Distinct449
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1014.9584
Minimum977.1
Maximum1038.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size174.0 KiB
2025-11-04T02:24:27.644842image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum977.1
5-th percentile1004.1
Q11010.1
median1014.8
Q31019.6
95-th percentile1026.4
Maximum1038.5
Range61.4
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation6.8465518
Coefficient of variation (CV)0.0067456477
Kurtosis0.070950789
Mean1014.9584
Median Absolute Deviation (MAD)4.7
Skewness0.034046713
Sum22583840
Variance46.875272
MonotonicityNot monotonic
2025-11-04T02:24:27.836809image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1012.2 149
 
0.7%
1013.9 143
 
0.6%
1012.8 142
 
0.6%
1014.8 141
 
0.6%
1015.3 140
 
0.6%
1013.7 138
 
0.6%
1017 136
 
0.6%
1018.2 136
 
0.6%
1015.6 135
 
0.6%
1014.4 133
 
0.6%
Other values (439) 20858
93.7%
ValueCountFrequency (%)
977.1 1
< 0.1%
979 1
< 0.1%
981.9 1
< 0.1%
982.2 1
< 0.1%
983.2 1
< 0.1%
986.6 1
< 0.1%
987.1 1
< 0.1%
987.3 1
< 0.1%
988 2
< 0.1%
988.8 1
< 0.1%
ValueCountFrequency (%)
1038.5 1
 
< 0.1%
1037.3 1
 
< 0.1%
1036.2 1
 
< 0.1%
1036.1 1
 
< 0.1%
1036 3
< 0.1%
1035.9 2
< 0.1%
1035.8 1
 
< 0.1%
1035.7 4
< 0.1%
1035.6 1
 
< 0.1%
1035.5 1
 
< 0.1%

Cloud9am
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.227046
Minimum0
Maximum8
Zeros2072
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size174.0 KiB
2025-11-04T02:24:27.970351image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q37
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.7933707
Coefficient of variation (CV)0.66083281
Kurtosis-1.555071
Mean4.227046
Median Absolute Deviation (MAD)2
Skewness-0.15266651
Sum94056
Variance7.8029197
MonotonicityNot monotonic
2025-11-04T02:24:28.082968image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
7 5606
25.2%
1 4257
19.1%
8 2150
 
9.7%
6 2107
 
9.5%
0 2072
 
9.3%
2 1711
 
7.7%
3 1635
 
7.3%
5 1536
 
6.9%
4 1177
 
5.3%
ValueCountFrequency (%)
0 2072
 
9.3%
1 4257
19.1%
2 1711
 
7.7%
3 1635
 
7.3%
4 1177
 
5.3%
5 1536
 
6.9%
6 2107
 
9.5%
7 5606
25.2%
8 2150
 
9.7%
ValueCountFrequency (%)
8 2150
 
9.7%
7 5606
25.2%
6 2107
 
9.5%
5 1536
 
6.9%
4 1177
 
5.3%
3 1635
 
7.3%
2 1711
 
7.7%
1 4257
19.1%
0 2072
 
9.3%

Cloud3pm
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3406139
Minimum0
Maximum9
Zeros1409
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size174.0 KiB
2025-11-04T02:24:28.197032image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.6443196
Coefficient of variation (CV)0.60920407
Kurtosis-1.4577918
Mean4.3406139
Median Absolute Deviation (MAD)2
Skewness-0.2058145
Sum96583
Variance6.9924264
MonotonicityNot monotonic
2025-11-04T02:24:28.318255image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 5468
24.6%
1 3914
17.6%
6 2480
11.1%
2 1927
 
8.7%
5 1915
 
8.6%
3 1901
 
8.5%
8 1857
 
8.3%
0 1409
 
6.3%
4 1379
 
6.2%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 1409
 
6.3%
1 3914
17.6%
2 1927
 
8.7%
3 1901
 
8.5%
4 1379
 
6.2%
5 1915
 
8.6%
6 2480
11.1%
7 5468
24.6%
8 1857
 
8.3%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 1857
 
8.3%
7 5468
24.6%
6 2480
11.1%
5 1915
 
8.6%
4 1379
 
6.2%
3 1901
 
8.5%
2 1927
 
8.7%
1 3914
17.6%
0 1409
 
6.3%

Temp9am
Real number (ℝ)

High correlation 

Distinct370
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.200917
Minimum-0.7
Maximum39.4
Zeros1
Zeros (%)< 0.1%
Negative5
Negative (%)< 0.1%
Memory size174.0 KiB
2025-11-04T02:24:28.469257image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-0.7
5-th percentile8.1
Q113
median17.8
Q323.4
95-th percentile29.1
Maximum39.4
Range40.1
Interquartile range (IQR)10.4

Descriptive statistics

Standard deviation6.6081716
Coefficient of variation (CV)0.36306806
Kurtosis-0.76128499
Mean18.200917
Median Absolute Deviation (MAD)5.1
Skewness0.096262171
Sum404988.6
Variance43.667932
MonotonicityNot monotonic
2025-11-04T02:24:28.654873image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.6 136
 
0.6%
16.7 133
 
0.6%
14.7 132
 
0.6%
17 129
 
0.6%
18.3 129
 
0.6%
15.9 128
 
0.6%
13.8 126
 
0.6%
15.5 126
 
0.6%
14.1 125
 
0.6%
16.6 125
 
0.6%
Other values (360) 20962
94.2%
ValueCountFrequency (%)
-0.7 2
< 0.1%
-0.5 1
< 0.1%
-0.2 1
< 0.1%
-0.1 1
< 0.1%
0 1
< 0.1%
0.4 1
< 0.1%
0.6 1
< 0.1%
0.7 1
< 0.1%
0.8 1
< 0.1%
0.9 2
< 0.1%
ValueCountFrequency (%)
39.4 1
< 0.1%
39.1 1
< 0.1%
39 1
< 0.1%
38.9 1
< 0.1%
38 1
< 0.1%
37.9 1
< 0.1%
37.7 2
< 0.1%
37.6 1
< 0.1%
36.9 1
< 0.1%
36.8 1
< 0.1%

Temp3pm
Real number (ℝ)

High correlation 

Distinct381
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.70124
Minimum3.7
Maximum46.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size174.0 KiB
2025-11-04T02:24:28.834528image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum3.7
5-th percentile12.1
Q117.3
median22.4
Q327.9
95-th percentile33.9
Maximum46.1
Range42.4
Interquartile range (IQR)10.6

Descriptive statistics

Standard deviation6.9075466
Coefficient of variation (CV)0.30428058
Kurtosis-0.60773798
Mean22.70124
Median Absolute Deviation (MAD)5.3
Skewness0.18551576
Sum505125.3
Variance47.7142
MonotonicityNot monotonic
2025-11-04T02:24:29.024062image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 137
 
0.6%
21.6 129
 
0.6%
21.7 128
 
0.6%
18.4 125
 
0.6%
18.5 123
 
0.6%
18.7 123
 
0.6%
24.7 123
 
0.6%
16.8 122
 
0.5%
19 122
 
0.5%
22.1 121
 
0.5%
Other values (371) 20998
94.4%
ValueCountFrequency (%)
3.7 1
 
< 0.1%
4.8 1
 
< 0.1%
5.1 1
 
< 0.1%
5.7 1
 
< 0.1%
5.8 2
< 0.1%
6 2
< 0.1%
6.2 3
< 0.1%
6.4 1
 
< 0.1%
6.6 1
 
< 0.1%
6.7 3
< 0.1%
ValueCountFrequency (%)
46.1 2
< 0.1%
45.8 2
< 0.1%
45.4 1
< 0.1%
45.3 1
< 0.1%
45.2 1
< 0.1%
44.8 1
< 0.1%
44.5 1
< 0.1%
44.1 2
< 0.1%
43.9 1
< 0.1%
43.7 1
< 0.1%

RainToday
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.9 KiB
False
17304 
True
4947 
ValueCountFrequency (%)
False 17304
77.8%
True 4947
 
22.2%
2025-11-04T02:24:29.171678image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

RainTomorrow
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.9 KiB
False
17301 
True
4950 
ValueCountFrequency (%)
False 17301
77.8%
True 4950
 
22.2%
2025-11-04T02:24:29.295793image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Date_day
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4078918
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size174.0 KiB
2025-11-04T02:24:29.419858image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4530168
Coefficient of variation (CV)0.53886939
Kurtosis-1.2231334
Mean6.4078918
Median Absolute Deviation (MAD)3
Skewness0.019981043
Sum142582
Variance11.923325
MonotonicityNot monotonic
2025-11-04T02:24:29.538785image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 2034
9.1%
1 2019
9.1%
5 1897
8.5%
11 1881
8.5%
9 1867
8.4%
6 1854
8.3%
10 1837
8.3%
8 1815
8.2%
7 1806
8.1%
4 1782
8.0%
Other values (2) 3459
15.5%
ValueCountFrequency (%)
1 2019
9.1%
2 1781
8.0%
3 2034
9.1%
4 1782
8.0%
5 1897
8.5%
6 1854
8.3%
7 1806
8.1%
8 1815
8.2%
9 1867
8.4%
10 1837
8.3%
ValueCountFrequency (%)
12 1678
7.5%
11 1881
8.5%
10 1837
8.3%
9 1867
8.4%
8 1815
8.2%
7 1806
8.1%
6 1854
8.3%
5 1897
8.5%
4 1782
8.0%
3 2034
9.1%

Date_month
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5153926
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size174.0 KiB
2025-11-04T02:24:29.703735image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4455604
Coefficient of variation (CV)0.52883389
Kurtosis-1.2160918
Mean6.5153926
Median Absolute Deviation (MAD)3
Skewness-0.0076459381
Sum144974
Variance11.871887
MonotonicityNot monotonic
2025-11-04T02:24:29.833067image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9 1883
8.5%
10 1879
8.4%
11 1867
8.4%
6 1864
8.4%
7 1862
8.4%
2 1859
8.4%
8 1854
8.3%
3 1850
8.3%
4 1849
8.3%
12 1834
8.2%
Other values (2) 3650
16.4%
ValueCountFrequency (%)
1 1818
8.2%
2 1859
8.4%
3 1850
8.3%
4 1849
8.3%
5 1832
8.2%
6 1864
8.4%
7 1862
8.4%
8 1854
8.3%
9 1883
8.5%
10 1879
8.4%
ValueCountFrequency (%)
12 1834
8.2%
11 1867
8.4%
10 1879
8.4%
9 1883
8.5%
8 1854
8.3%
7 1862
8.4%
6 1864
8.4%
5 1832
8.2%
4 1849
8.3%
3 1850
8.3%

Date_weekday
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0020224
Minimum0
Maximum6
Zeros3154
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size174.0 KiB
2025-11-04T02:24:29.952872image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9996731
Coefficient of variation (CV)0.66610866
Kurtosis-1.2475415
Mean3.0020224
Median Absolute Deviation (MAD)2
Skewness0.0038966457
Sum66798
Variance3.9986925
MonotonicityNot monotonic
2025-11-04T02:24:30.109028image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 3230
14.5%
6 3213
14.4%
1 3188
14.3%
2 3186
14.3%
0 3154
14.2%
5 3150
14.2%
4 3130
14.1%
ValueCountFrequency (%)
0 3154
14.2%
1 3188
14.3%
2 3186
14.3%
3 3230
14.5%
4 3130
14.1%
5 3150
14.2%
6 3213
14.4%
ValueCountFrequency (%)
6 3213
14.4%
5 3150
14.2%
4 3130
14.1%
3 3230
14.5%
2 3186
14.3%
1 3188
14.3%
0 3154
14.2%

Date_hour
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0.0
22251 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters66753
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 22251
100.0%

Length

2025-11-04T02:24:30.276489image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-04T02:24:30.403254image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 22251
100.0%

Most occurring characters

ValueCountFrequency (%)
0 44502
66.7%
. 22251
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44502
66.7%
Other Punctuation 22251
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 44502
100.0%
Other Punctuation
ValueCountFrequency (%)
. 22251
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 66753
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 44502
66.7%
. 22251
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66753
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44502
66.7%
. 22251
33.3%

Date_minute
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0.0
22251 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters66753
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 22251
100.0%

Length

2025-11-04T02:24:30.541684image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-04T02:24:30.658854image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 22251
100.0%

Most occurring characters

ValueCountFrequency (%)
0 44502
66.7%
. 22251
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44502
66.7%
Other Punctuation 22251
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 44502
100.0%
Other Punctuation
ValueCountFrequency (%)
. 22251
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 66753
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 44502
66.7%
. 22251
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66753
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44502
66.7%
. 22251
33.3%

Interactions

2025-11-04T02:24:19.628145image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:12.359888image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:17.348146image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:22.173922image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:28.694983image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:34.536816image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:39.835697image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:44.894569image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:50.429075image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:55.617213image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:58.116901image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:00.394919image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:02.862545image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:05.311813image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:07.937719image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:10.185611image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:12.564611image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:14.843040image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:17.368330image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:19.761749image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:12.618362image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:17.607843image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:22.429330image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:28.971411image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:34.784221image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:40.112260image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:45.191052image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:50.712353image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:55.785849image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:58.226919image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:00.524309image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:03.008930image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:05.428673image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:08.053232image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:10.331760image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:12.671274image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:14.957990image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:17.481057image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:19.880279image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:12.859613image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:17.821360image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:22.689405image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:29.253459image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:35.024506image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:40.345490image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:45.488513image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:50.986599image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:55.917433image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:58.318680image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:00.669765image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:03.144510image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:05.521284image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:08.192268image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:10.432824image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:12.807281image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:15.062243image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:17.598662image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:20.065780image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:13.137247image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:18.087565image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:22.961371image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:29.617956image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:35.307074image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:40.621125image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:45.790556image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:51.292619image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:56.056735image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:58.468814image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:00.839593image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:03.252208image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:05.898504image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:08.304940image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:10.555673image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:12.914803image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:15.187971image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:17.718295image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:20.177906image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:13.406308image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:18.359239image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:23.233499image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:29.899886image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:35.573002image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:40.933827image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:46.088611image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:51.573183image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:56.252941image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:58.591395image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:00.949114image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:03.394859image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:06.010488image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:08.449183image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:10.670455image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:13.019557image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:15.312441image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:17.867046image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:20.315838image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:13.660319image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:18.615149image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:23.508229image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:30.164517image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:35.856572image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:41.170891image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-11-04T02:24:19.158474image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:21.570593image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:16.530984image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:21.381248image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:27.751570image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:33.753576image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:39.023379image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:44.124111image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:49.534285image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:55.008312image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:57.712933image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:00.009835image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:02.460165image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:04.920771image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:07.580714image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:09.883001image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:12.188977image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:14.451077image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:16.999409image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:19.275082image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:21.734662image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:16.773761image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:21.631542image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:28.027628image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:34.025800image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:39.266351image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:44.376571image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:49.840062image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:55.357746image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:57.839326image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:00.129849image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:02.584171image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:05.065076image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:07.692395image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:09.975701image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:12.309380image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:14.578646image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:17.121649image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:19.390501image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:21.836190image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:17.048728image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:21.918161image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:28.302571image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:34.264483image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:39.546109image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:44.637317image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:50.140117image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:55.498561image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:23:57.986375image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:00.243247image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:02.703409image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:05.184356image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:07.802867image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:10.079953image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:12.431621image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:14.698265image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:17.242804image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-04T02:24:19.505807image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-11-04T02:24:30.768375image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Cloud3pmCloud9amDate_dayDate_monthDate_weekdayEvaporationHumidity3pmHumidity9amLocationMaxTempMinTempPressure3pmPressure9amRainTodayRainTomorrowRainfallSunshineTemp3pmTemp9amWindDir3pmWindDir9amWindGustDirWindGustSpeedWindSpeed3pmWindSpeed9am
Cloud3pm1.0000.635-0.0030.004-0.006-0.2240.5300.3750.144-0.2620.035-0.096-0.1610.2850.4150.333-0.714-0.303-0.1120.0470.0580.0560.1080.0320.046
Cloud9am0.6351.000-0.0110.006-0.006-0.2140.5260.4740.161-0.2630.092-0.073-0.1430.3140.3360.368-0.692-0.281-0.1180.0450.0620.0500.0750.0670.019
Date_day-0.003-0.0111.000-0.0010.001-0.108-0.012-0.0550.000-0.220-0.2580.0990.1200.0430.0360.020-0.005-0.227-0.2000.0760.0760.0840.0040.0250.018
Date_month0.0040.006-0.0011.0000.004-0.0070.0040.0090.000-0.005-0.002-0.011-0.0090.0070.0190.004-0.010-0.006-0.0040.0160.0110.0070.0050.0020.001
Date_weekday-0.006-0.0060.0010.0041.0000.0100.0010.0060.000-0.0010.0030.0180.0120.0110.0130.0060.008-0.000-0.0010.0160.0200.021-0.011-0.013-0.010
Evaporation-0.224-0.214-0.108-0.0070.0101.000-0.406-0.5600.1420.7060.580-0.369-0.3360.1540.112-0.3170.4560.6860.6670.0350.0660.0410.2330.1580.182
Humidity3pm0.5300.526-0.0120.0040.001-0.4061.0000.6610.221-0.3990.0860.012-0.0770.3960.5130.462-0.623-0.444-0.1180.0570.0900.048-0.0500.058-0.056
Humidity9am0.3750.474-0.0550.0090.006-0.5600.6611.0000.205-0.448-0.1600.1400.0810.4150.2940.476-0.546-0.442-0.3820.0580.0800.060-0.195-0.092-0.236
Location0.1440.1610.0000.0000.0000.1420.2210.2051.0000.2730.2780.1390.1240.1750.1710.0390.1390.2730.2760.2020.2240.2110.1450.1660.176
MaxTemp-0.262-0.263-0.220-0.005-0.0010.706-0.399-0.4480.2731.0000.767-0.483-0.3860.2330.161-0.2890.4930.9860.9040.1250.1340.1250.0490.0280.001
MinTemp0.0350.092-0.258-0.0020.0030.5800.086-0.1600.2780.7671.000-0.522-0.5070.0920.1070.0190.1200.7450.9110.1260.1090.1240.1450.1580.127
Pressure3pm-0.096-0.0730.099-0.0110.018-0.3690.0120.1400.139-0.483-0.5221.0000.9600.1250.220-0.066-0.057-0.456-0.5240.0980.0700.080-0.323-0.223-0.125
Pressure9am-0.161-0.1430.120-0.0090.012-0.336-0.0770.0810.124-0.386-0.5070.9601.0000.1950.244-0.1600.012-0.351-0.4700.0870.0670.077-0.371-0.268-0.167
RainToday0.2850.3140.0430.0070.0110.1540.3960.4150.1750.2330.0920.1250.1951.0000.3110.3170.3410.2400.1240.1360.1930.1630.1540.0920.083
RainTomorrow0.4150.3360.0360.0190.0130.1120.5130.2940.1710.1610.1070.2200.2440.3111.0000.1830.4560.1990.0620.0960.1210.1050.2210.0960.074
Rainfall0.3330.3680.0200.0040.006-0.3170.4620.4760.039-0.2890.019-0.066-0.1600.3170.1831.000-0.403-0.298-0.1500.0080.0180.0140.1390.0990.083
Sunshine-0.714-0.692-0.005-0.0100.0080.456-0.623-0.5460.1390.4930.120-0.0570.0120.3410.456-0.4031.0000.5160.3290.0690.0870.078-0.0300.0230.011
Temp3pm-0.303-0.281-0.227-0.006-0.0000.686-0.444-0.4420.2730.9860.745-0.456-0.3510.2400.199-0.2980.5161.0000.8820.1280.1360.1270.0190.010-0.007
Temp9am-0.112-0.118-0.200-0.004-0.0010.667-0.118-0.3820.2760.9040.911-0.524-0.4700.1240.062-0.1500.3290.8821.0000.1320.1200.1310.1020.1290.067
WindDir3pm0.0470.0450.0760.0160.0160.0350.0570.0580.2020.1250.1260.0980.0870.1360.0960.0080.0690.1280.1321.0000.2030.3580.0800.0610.057
WindDir9am0.0580.0620.0760.0110.0200.0660.0900.0800.2240.1340.1090.0700.0670.1930.1210.0180.0870.1360.1200.2031.0000.2570.0900.0790.081
WindGustDir0.0560.0500.0840.0070.0210.0410.0480.0600.2110.1250.1240.0800.0770.1630.1050.0140.0780.1270.1310.3580.2571.0000.0870.0590.074
WindGustSpeed0.1080.0750.0040.005-0.0110.233-0.050-0.1950.1450.0490.145-0.323-0.3710.1540.2210.139-0.0300.0190.1020.0800.0900.0871.0000.6800.589
WindSpeed3pm0.0320.0670.0250.002-0.0130.1580.058-0.0920.1660.0280.158-0.223-0.2680.0920.0960.0990.0230.0100.1290.0610.0790.0590.6801.0000.461
WindSpeed9am0.0460.0190.0180.001-0.0100.182-0.056-0.2360.1760.0010.127-0.125-0.1670.0830.0740.0830.011-0.0070.0670.0570.0810.0740.5890.4611.000

Missing values

2025-11-04T02:24:22.096249image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-04T02:24:22.578623image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

LocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRainTomorrowDate_dayDate_monthDate_weekdayDate_hourDate_minute
0Cobar17.935.20.012.012.3SSW48.0ENESW6.020.020.013.01006.31004.42.05.026.633.4NoNo1.01.03.00.00.0
1Cobar18.428.90.014.813.0S37.0SSESSE19.019.030.08.01012.91012.11.01.020.327.0NoNo1.02.06.00.00.0
2Cobar19.437.60.010.810.6NNE46.0NNENNW30.015.042.022.01012.31009.21.06.028.734.9NoNo1.04.02.00.00.0
3Cobar21.938.40.011.412.2WNW31.0WNWWSW6.06.037.022.01012.71009.11.05.029.135.6NoNo1.05.04.00.00.0
4Cobar24.241.00.011.28.4WNW35.0NWWNW17.013.019.015.01010.71007.41.06.033.637.6NoNo1.06.00.00.00.0
5Cobar27.136.10.013.00.0N43.0NWNW7.020.026.019.01007.71007.48.08.030.734.3NoNo1.07.02.00.00.0
6Cobar23.334.00.09.812.6SSW41.0SSSE17.019.033.015.01011.31009.93.01.025.031.5NoNo1.08.05.00.00.0
7Cobar16.134.20.014.613.2SE37.0SES15.06.025.09.01013.31009.21.01.020.732.8NoNo1.09.01.00.00.0
8Cobar19.035.50.012.012.3ENE48.0ENEWSW30.09.046.028.01008.31004.01.05.023.433.3NoNo1.010.03.00.00.0
9Cobar19.735.50.011.012.7NE41.0NNEWSW15.017.061.014.01007.91005.81.05.024.033.6NoNo1.011.06.00.00.0
LocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRainTomorrowDate_dayDate_monthDate_weekdayDate_hourDate_minute
22241Darwin22.731.60.07.07.4ESE43.0SEESE19.019.042.037.01015.51011.47.07.024.030.9NoNo6.03.00.00.00.0
22242Darwin22.431.40.06.68.3E43.0ESEE24.017.059.040.01013.91010.46.07.025.830.8NoNo6.04.03.00.00.0
22243Darwin23.332.40.05.48.9E43.0EE20.024.066.032.01014.51011.13.03.025.632.1NoNo6.05.05.00.00.0
22244Darwin20.631.80.06.410.8E46.0SEESE15.017.045.026.01016.11012.61.01.024.231.0NoNo6.06.01.00.00.0
22245Darwin20.030.40.09.010.8ESE43.0SEENE19.019.048.038.01016.21012.51.01.023.430.1NoNo6.07.03.00.00.0
22246Darwin19.229.40.07.410.9E54.0ESEE24.019.043.028.01016.21012.72.04.023.328.8NoNo6.08.06.00.00.0
22247Darwin20.629.40.010.65.4E46.0SEESE17.022.046.026.01016.51013.07.05.021.628.5NoNo6.09.02.00.00.0
22248Darwin18.729.40.07.88.7ESE48.0SEE22.017.029.024.01017.11013.57.07.020.928.5NoNo6.010.04.00.00.0
22249Darwin19.029.40.07.610.4E46.0ESESE20.017.031.024.01017.31013.93.01.021.828.6NoNo6.011.00.00.00.0
22250Darwin17.229.10.07.210.1ESE44.0ESEE15.020.041.021.01017.61014.11.02.020.928.5NoNo6.012.02.00.00.0